Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8, 24-35 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 13 May 2025; Revised: 18 June 2025; Accepted: 23 June 2025; Published: 1 August 2025 * Correspondence: shahriman.z.a@uitm.edu.my Traditional crafts in digital commerce: Consumer value and behavior on TikTok China's ceramic live-streaming Fang Limin1, Shahriman Zainal Abidin1*, Zahirah Harun1, Chen Xiumian1 1Faculty of Art & Design, Universiti Teknologi MARA, 40450 Shah Alam, Selangor Darul Ehsan, Malaysia; shahriman.z.a@uitm.edu.my (S.Z.A.). Abstract: This paper analyzes how TikTok China's live-streaming platform transforms the marketing of Jingdezhen ceramics. Using Structural Equation Modeling (SEM), the research examines how consumers' perceived value influences their purchase satisfaction and subsequent behavior during Jingdezhen ceramics live-streaming sessions. The model identifies and quantifies key influence pathways, revealing that perceived enjoyment during live-streaming is a crucial mediating variable affecting consumers' perceived value and purchase satisfaction of Jingdezhen ceramics. The study validates the significant impact of integrating Jingdezhen ceramics' traditional cultural characteristics with live-streaming presentation methods on consumer purchase decisions through rigorous SEM analysis. The statistical modeling reveals causal relationships among ceramic value perception, live- streaming interaction experience, and purchase outcomes. While the research is limited to Jingdezhen ceramic products, this SEM-based approach provides empirical evidence for optimizing traditional ceramic product marketing on digital platforms while offering theoretical guidance and practical implications for Jingdezhen ceramics' inheritance and innovation in the digital era. Keywords: Consumer satisfaction, Jingdezhen ceramics, Live-streaming Commerce, Perceived value, Structural equation modeling. 1. Introduction Jingdezhen, China's historical center of porcelain production , marked a significant milestone when designated as the 'Ceramic Culture Inheritance and Innovation Pilot Zone' in 2019 [1]. This initiative, aligned with China's 14th Five-Year Plan, emphasizes the digitalization of ceramic cultural heritage in the modern era [2]. The transformation has been particularly evident in the emergence of live- streaming retail [3] where traditional craft marketing meets digital innovation. TikTok China, with over 600 million daily active users [4] has become a pivotal platform for this digital transformation, particularly in the ceramic industry. Ceramic is one of product design needs certain quality of aesthetics on form-giving that reflects to the human sensation [5]. The integration of Jingdezhen's ceramic trade with live-streaming commerce has shown remarkable growth, with sales reaching 3.067 billion yuan in 2021 and doubling the following year [4]. This "ceramics + e-commerce + live streaming" model represents a significant innovation in traditional craft marketing. However, this rapid digital transformation has introduced new challenges, including inconsistent streaming quality, insufficient professional hosts, and discrepancies between online and offline consumer experiences of ceramic products. This may can be related to the attributes of unconscious interaction between human cognition and behavior in everyday product[6]. This research applies Structural Equation Modeling (SEM) to analyze how consumers perceive and value Jingdezhen ceramics through TikTok China's live-streaming platform [2]. Specifically, it investigates the relationships between perceived value, enjoyment, purchase satisfaction, and post- purchase behavior. The findings aim to provide practical insights for optimizing product presentation, https://orcid.org/0000-0002-6752-3709 https://orcid.org/0009-0006-7802-1321 25 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate streaming environments, and overall consumer experience in the digital marketing of traditional ceramics. 2. Related Work Perceived value theory serves as a crucial foundation for consumer behavior research. The seminal research by Sheth, et al. [7] deconstructed consumer perceived value into emotional, social, functional, cognitive, and contextual dimensions, creating a foundation for future studies [7]. With the development of live-streaming retail, scholars have begun to focus on consumer behavioral characteristics in this emerging field [8] found that perceived value in the live-streaming shopping environment exhibits new features, with interactivity and immediacy becoming key factors influencing consumer decision-making [8]. It can be seen through the case for intuition-driven design expertise [9]. In the context of live-streaming retail, Perceived Enjoyment (PE) demonstrates unique value. Xi, et al. [10] empirically confirmed that the entertainment and social aspects of live-streaming shopping significantly influence consumers' purchase intentions Xi, et al. [10]. Tian and Frank [11] further revealed that real-time interaction between streamers and viewers enhances user enjoyment experience, thereby facilitating purchase decisions [11]. Live-streaming Interactivity (LI), as a crucial characteristic, live-streaming Interactivity (LI) has received extensive attention. Through questionnaire surveys, Fan, et al. [12] discovered that real-time interaction in live-streaming rooms significantly affects consumer trust and purchase intention Fan, et al. [12]. Wu, et al. [13] indicated that different forms of interaction (such as bullet screens, virtual gifts, and Q&A) have varying degrees of influence on consumer behavior Wu, et al. [13]. Zhang, et al. [14] found that real-time interaction in live-streaming shopping enhances consumer purchase confidence more effectively than traditional e-commerce [14]. Product Authenticity (PA) plays a vital role in live-streaming retail. Liu and Sun [15] demonstrated that live demonstrations significantly enhance consumers' perception of product authenticity Liu and Sun [15]. Hamidah, et al. [16] pointed out that streamers' professional presentation and authentic interaction effectively reduce consumers' perceived risk [16]. Cultural products possess unique characteristics in live-streaming retail. Yingqing, et al. [17] found that presenting traditional cultural elements enhances product perceived value [17]. Through case analysis, Li, et al. [18] confirmed that cultural identity plays a crucial role in live-streaming sales of artistic products [18]. Regarding satisfaction and post-purchase behavior research, Li, et al. [19] discovered through questionnaire surveys that interaction quality in live-streaming shopping is significantly correlated with consumer satisfaction Li, et al. [19]. Yi, et al. [20] showed that live-streaming shopping satisfaction significantly influences consumers' repurchase intentions Yi, et al. [20]. Gallarza and Saura [21] empirically confirmed that word-of-mouth communication in social media environments has a significant impact on post-purchase behavior. The above research indicates that consumer behavior in live-streaming retail environments exhibits new characteristics, with factors such as perceived value, interactivity, and authenticity jointly influencing consumers' purchase decisions and post-purchase behavior. Based on these existing literature findings, this study constructs a consumer behavior research model adapted to live-streaming retail, providing a theoretical foundation for understanding consumer behavior in the new retail environment. 3. Methodology 3.1. Theoretical Framework Structural equation modeling (SEM), a statistical method for analyzing linear relationships between observed and latent variables, has been extensively validated in studies of customer satisfaction and behavioral intentions [21]. Based on the unique characteristics of Jingdezhen TikTok (China) short 26 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate video commerce, our research model (Fig.1) examines the following relationships: Four key factors - product attributes, cultural elements, interactive features, and authenticity - shape consumers' perceived value in live-streaming environments. These factors, enhanced by perceived enjoyment, influence both purchase satisfaction and subsequent consumer behavior. The model further posits that satisfaction directly impacts post-purchase actions. Figure 1. Research model of this study. 3.2. Hypothetical Based on prior literature, we developed the following hypotheses, with their proposed relationships illustrated in Figure 1. Hypotheses Related to Perceived Enjoyment (PE) H1: PE → PS (Purchase Satisfaction) H2: PE → PC (Product Characteristics) H3: PE → LI (Live-streaming Interactivity) H4: PE → PA (Product Authenticity) H5: PE → CC (Cultural Characteristics) H6: PE → PB (Post-purchase Behavior) Hypotheses Related to Purchase Satisfaction (PS) H7: PC → PS H8: LI → PS H9: PA → PS H10: CC → PS Hypotheses Related to Post-purchase Behavior (PB) H11: PS → PB H12: CC → PB H13: PA → PB H14: PC → PB H15: LI → PB 27 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate 3.3. Questionnaire The empirical investigation utilized questionnaire data to examine how TikTok (China) platform characteristics influence consumers' value perception, perceived hedonism, purchase satisfaction, and post-purchase behavior. Data collection was conducted between March and May 2023, yielding 455 valid responses from 658 participants after excluding invalid submissions. The sample size exceeded the minimum requirement for structural equation modeling, being 30 times the number of questionnaire items [22]. Our instrument measured demographics (gender, age, education, monthly income) and platform- specific items (price, viewing frequency, ceramic product-related streaming preferences). Using a 7-point Likert scale, we measured eight constructs: authenticity, product characteristics, culture, interactivity, value perception, perceived enjoyment, purchase satisfaction, and post-purchase behavior. The measurement items were adapted from validated literature. 3.4. Date Collection Based on a survey of 455 respondents, the demographics reveal distinctive characteristics: a slightly higher proportion of females (55.4%), predominantly young adults aged 20-30 (48.4%), with a mean age of 30.2 years. The sample demonstrates high educational attainment, with over 60% holding bachelor's degrees or above. Regarding usage patterns, the average viewing duration is 2.6 hours, with more than 60% of users spending less than 3 hours daily. Preferences for ceramic products show diverse interests, averaging 3.8 categories per user, with coffee ware (78.7%) and ceramic handicrafts (58.9%) being the most popular choices. These findings paint a portrait of a well-educated, young user base characterized by diverse product interests and moderate usage habits (Table 1). Table 1. Demographic Profile of Participants (N=455). Sample Category Number Percentage Gender Male 203 44.60% Female 252 55.40% Age Under 20 60 13.20% 20-30 220 48.40% 31-40 116 25.50% 41-50 35 7.70% over 50 24 5.30% Education High school degree 88 19.30% Junior college 81 17.80% Bachelor's degree 207 45.50% Master's degree 55 12.10% Doctoral degree 24 5.30% Viewing Frequency Under1hour 163 35.80% 1-3hour 124 27.30% 3-5hour 97 21.30% 5-7hour 46 10.10% Above 7hour 25 5.50% Favourite Ceramic production Tableware 237 52.10% Coffee ware 358 78.70% Tea ware 247 54.30% Ceramic Accessories 254 55.80% Ceramic Handicrafts 268 58.90% Others 378 83.10% 28 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate 3. Analysis and Results 3.1. Reliability Analysis We assessed measurement reliability using SPSS (Version 24.0). The Cronbach's α values for all constructs exceeded 0.9, substantially surpassing Hair [23] recommended threshold of 0.66. Item- total correlations were above 0.8, exceeding the standard criterion of 0.5. Additionally, removing any item would decrease the scale's alpha value, confirming that all items should be retained. These results demonstrate robust internal consistency of our measurement scales (Table 2). Table 2. Reliability Analysis. Dimension Items Corrected Item-to-Total Correlation Cronbach’s α if Item Deleted Cronbach’s α PC PC1 0.781 0.879 0.905 PC2 0.721 0.892 PC3 0.787 0.878 PC4 0.774 0.881 PC5 0.744 0.887 CC CC1 0.818 0.885 0.914 CC2 0.826 0.879 CC3 0.840 0.867 PA PA1 0.784 0.823 0.883 PA2 0.772 0.834 PA3 0.761 0.844 LI LI1 0.803 0.901 0.92 LI2 0.806 0.9 LI3 0.794 0.902 LI4 0.794 0.902 LI5 0.772 0.907 PE PE1 0.882 0.919 0.941 PE2 0.832 0.928 PE3 0.829 0.929 PE4 0.837 0.927 PE5 0.822 0.931 PS PS1 0.884 0.919 0.941 PS2 0.828 0.929 PS3 0.849 0.925 PS4 0.842 0.927 PS5 0.796 0.935 PB PB1 0.813 0.892 0.917 PB2 0.787 0.898 PB3 0.811 0.893 PB4 0.81 0.893 PB5 0.713 0.913 3.2. Exploratory Factor Analysis SPSS 24.0 was used to conduct the Kaiser-Meyer-Olkin (KMO) test and Bartlett's test of sphericity (Table 3). The KMO values ranged from 0.868 to 0.909, substantially exceeding the threshold of 0.5. Bartlett's test yielded significance levels approaching zero (p < 0.05), supporting the data's suitability for factor analysis [24, 25]. Principal component analysis revealed four factors with eigenvalues greater than 1 for each variable. The cumulative variance explained exceeded 50%, with item communalities above 0.5 and factor loadings above 0.6, meeting the criteria proposed by [26]. These results confirm the construct validity for subsequent analyses. 29 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate Table 3. Exploratory Factor Analysis. Dimension Items KMO Bartlett Sphere Test Factor Loading Commonality Eigenvalue Total variation explained％ PC PC1 0.868 0 0.865 0.748 3.625 72.50% PC2 0.821 0.675 PC3 0.87 0.757 PC4 0.861 0.741 PC5 0.839 0.704 CC CC1 0.759 0 0.845 0.919 2.563 85.44% CC2 0.853 0.923 CC3 0.866 0.930 PA PA1 0.903 0 0.821 0.906 2.43 81.00% PA2 0.81 0.9 PA3 0.799 0.894 LI LI1 0.895 0 0.878 0.771 3.794 75.88% LI2 0.88 0.774 LI3 0.871 0.759 LI4 0.871 0.759 LI5 0.855 0.731 PE PE1 0.896 0 0.928 0.86 4.046 80.92% PE2 0.893 0.798 PE3 0.892 0.795 PE4 0.898 0.806 PE5 0.887 0.786 PS PS1 0.909 0 0.929 0.864 4.043 80.87% PS2 0.891 0.794 PS3 0.906 0.82 PS4 0.901 0.812 PS5 0.868 0.753 PB PB1 0.89 0 0.887 0.787 3.761 75.22% PB2 0.867 0.751 PB3 0.886 0.784 PB4 PB5 0.883 0.811 0.78 0.658 3.3. Confirmatory Factor We conducted confirmatory factor analysis using AMOS software (Table 4). Both unstandardized and standardized factor loadings exceeded 0.7, surpassing Chin [27] threshold of 0.5, indicating strong item-construct relationships [27]. The composite reliability (CR) values exceeded Hair [23] recommended threshold of 0.7, while the average variance extracted (AVE) for each construct surpassed the 0.5 criterion [28, 29]. These results demonstrate adequate convergent validity of our measurement model. 30 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate Table 4. Confirmatory Factor. Dimension Items Unstandardized Standardized S.E. p-Value AVE CR Factor Loading Factor Loading PC PC1 1 0.82 - - 0.657 0.905 PC2 0.948 0.764 0.052 0 PC3 0.966 0.84 0.046 0 PC4 0.969 0.823 0.047 0 PC5 0.938 0.804 0.048 0 CC CC1 1 0.882 - - 0.782 0.915 CC2 0.934 0.875 0.037 0 CC3 0.994 0.895 0.037 0 PA PA1 1 0.876 - - 0.721 0.886 PA2 0.962 0.84 0.043 0 PA3 0.968 0.831 0.044 0 LI LI1 1 0.854 - - 0.698 0.920 LI2 0.998 0.848 0.043 0 LI3 1.003 0.833 0.045 0 LI4 0.988 0.834 0.044 0 LI5 0.933 0.809 0.044 0 PE PE1 1 0.916 - - 0.762 0.941 PE2 0.893 0.869 0.031 0 PE3 0.892 0.856 0.032 0 PE4 0.905 0.864 0.032 0 PE5 0.968 0.857 0.035 0 PS PS1 1 0.919 - - 0.762 0.941 PS2 0.884 0.853 0.032 0 PS3 0.95 0.888 0.031 0 PS4 0.932 0.878 0.032 0 PS5 0.874 0.825 0.034 0 PB PB1 1 0.868 - - 0.6926 0.918 PB2 0.916 0.827 0.04 0 PB3 0.906 0.857 0.037 0 PB4 0.899 0.843 0.038 0 PB5 0.872 0.762 0.044 0 3.4. Differential Validity Following Fornell and Larcker [29] criterion, we assessed discriminant validity by comparing the square root of AVE with inter-construct correlations [29]. As shown in Table 5, all constructs (PC, PA, CC, LI, PE, PS, and PB) demonstrated significant correlations, while each construct's square root of AVE exceeded its correlations with other constructs, confirming adequate discriminant validity. 31 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate Table 5. Square Roots of the AVES Versus Correlations. PC CC PA LI PS PE PB Average Value Standard Deviation PC 0.811 5.340 1.296 CC 0.600** 0.884 5.4689 1.275 PA 0.499** 0.562** 0.849 5.063 1.243 LI 0.478** 0.483** 0.691** 0.836 5.126 1.218 PS 0.468** 0.538** 0.671** 0.679** 0.873 5.028 1.202 PE 0.478** 0.562** 0.672** 0.694** 0.818** 0.873 5.088 1.266 PB 0.508** 0.545** 0.582** 0.602** 0.700** 0.700** 0.832 5.236 1.200 Note: ** At the 0.01 level (two-tailed), the correlation was significant The bolded part of the diagonal line indicates the square root of AVE. We further assessed discriminant validity using the heterotrait-monotrait (HTMT) ratio calculated via PLS software (Table 6). All HTMT values fell below the 0.9 threshold recommended by Hamidah, et al. [16] providing additional support for discriminant validity. Table 6. Differential Validity Analysis. CC LI PA PB PC PE PS CC LI 0.533 PA 0.625 0.774 PB 0.595 0.689 0.645 PC 0.753 0.544 0.592 0.594 PE 0.606 0.767 0.737 0.753 0.562 PS 0.58 0.75 0.736 0.752 0.536 0.869 3.5. Comparison of Fit Degree Following Xiong, et al. [30] we examined common method bias by comparing two models: one without (M1) and one with (M2) common method factors (Table 7). The comparison revealed minimal differences in fit indices - changes in RFI, TLI, NFI, and CFI did not exceed 0.1, while RMSEA showed no reduction [30]. Although M2 demonstrated marginally better fit, these results suggest that common method bias was adequately controlled in our study. Table 7. M1 and M2 Comparison of Fit Degree. Common indices c2/df RMSEA RFI TLI NFI CFI SRMR Judgment criteria <5 <0.08 >0.9 >0.9 >0.9 >0.9 <0.08 M1 1.852 0.043 0.934 0.969 0.942 0.972 0.035 M2 1.759 0.042 0.936 0.971 0.944 0.974 0.03 3.6. Model Fit Degree The confirmatory factor analysis yielded fit indices (Table 8) that were evaluated against Kaiser [24] criteria: χ2/df < 5, RMSEA < 0.08, SRMR < 0.08, and RFI, TLI, NFI, CFI, and GFI > 0.9. All indices (Table 9) met these thresholds, confirming adequate structural validity of our measurement model. Table 8. Model Fit Degree. Common indices c2/df RMSEA RFI TLI NFI CFI SRMR Judgment criteria <5 <0.08 >0.9 >0.9 >0.9 >0.9 <0.08 CFA Value 2.421 0.063 0.914 0.948 0.923 0.953 0.056 32 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate 3.7. Structural Models Figure 2. Path Coefficient. Using AMOS with bootstrap bias-corrected percentile method (5,000 samples), the model testing revealed that 12 of 15 hypotheses were supported (Figure 2). Perceived enjoyment (PE) demonstrated significant direct effects (p < 0.05) on product characteristics (PC), cultural characteristics (CC), authenticity (PA), interactivity (LI), purchase satisfaction (PS), and post-purchase behavior (PB) (H1- H6). Interactivity and authenticity significantly influenced purchase satisfaction (H8-H9), while product characteristics and cultural aspects showed no significant impact (H7, H10). Purchase satisfaction, cultural characteristics, product features, and interactivity positively affected post-purchase behavior (H11, H12, H14, H15). However, product authenticity showed no significant effect on post-purchase behavior (H13). 33 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate Table 9. Hypothesis test results. Path Direct effect Indirect effect Total effect Support β B-C Sig. β B-C Sig. β B-C Sig. PE→PC 0.603 *** / / 0.603 *** YES PE→CC 0.642 *** / / 0.642 *** YES PE→PA 0.776 *** / / 0.776 *** YES PE→LI 0.791 *** / / 0.791 *** YES PC→PS -0.26 0.616 / / 0.026 0.473* NO CC→PS 0.013 0.862 / / 0.013 0.76 NO PA→PS 0.107 0.068 / / 0.107 0.033* YES LI→PS 0.113 0.066 / / 0.113 0.024* YES PE→PS 0.71 *** 0.165 0.019 0.875 *** YES PS→PB 0.278 0.003* / / 0.278 *** YES PE→PB 0.255 0.015* 0.504 *** 0.759 0.027* YES PC→PB 0.104 0.099 -0.007 0.616 0.150 *** YES CC→PB -0.041 0.588 0.004 0.862 0.108 0.024* YES PA→PB 0.165 0.049* 0.03 0.071 -0.012 0.5 NO LI→PB 0.255 0.037* 0.031 0.068 0.196 0.007* YES 4. Discussion The structural equation modeling results revealed several key findings. First, perceived enjoyment significantly influences product perception across multiple dimensions (H2-H5), particularly through interactivity and authenticity. The interaction between streamers and consumers emerged as a crucial factor in enhancing consumer experience, emphasizing the importance of timely, professional, and exclusive engagement. Purchase satisfaction is primarily driven by product authenticity and live-streaming interactivity (H7, H8), while product culture and characteristics showed minimal impact. Notably, perceived enjoyment demonstrated strong positive effects on both purchase satisfaction (H1) and post-purchase behavior (H6, path coefficient = 0.255), suggesting that entertainment experience significantly influences consumer loyalty and product advocacy. Purchase satisfaction showed a moderate influence on post-purchase behavior (H11, path coefficient ≈ 0.3), while product characteristics, culture, and interactivity exhibited weaker effects (H12, H14, H15, coefficients between 0.1-0.3). Interestingly, three hypotheses were not supported: product characteristics and culture did not significantly affect purchase satisfaction, and product authenticity showed no direct impact on post-purchase behavior (H7, H10, H13). This suggests that in TikTok (China)'s live-streaming context, comprehensive product presentation and real-time interaction may be more influential than traditional product attributes. 5. Conclusions Based on the analysis of 455 valid questionnaires, this study examined consumer behavior on TikTok (China)'s live-streaming platform for Jingdezhen ceramics. The findings contribute to three key areas: (1) Academic Impact: This research extends the understanding of online consumer behavior by applying structural equation modeling (SEM) to analyze the unique characteristics of TikTok (China)'s live-streaming platform, particularly highlighting the significant role of enjoyment in perceived value. (2) Market Implications: Results suggest that platforms should prioritize younger generations' entertainment needs and shopping experiences to enhance satisfaction and post-purchase engagement. The findings advocate for transforming live-streaming from a mere sales channel into an engaging cultural experience. 34 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate (3) Policy Recommendations: While TikTok (China) serves as a valuable platform for promoting Jingdezhen's ceramic culture, regulatory oversight is needed to maintain product quality and market integrity through appropriate policy frameworks. 6. Limitation and Future Research This study has several limitations. First, the sample was predominantly young respondents (73.9% under 40), potentially biasing results toward younger consumers. Second, future research could incorporate perceived trust as an additional variable and extend beyond Jingdezhen ceramics to other product categories. Finally, while this study included cultural elements, future research should explore cultural perception more deeply, particularly in the context of intangible cultural heritage. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] G. Gazette, "General Office of the People's Government of Jiangxi Province on the establishment of Jingdezhen National Ceramic Culture Heritage Innovation Pilot Zone Construction Leading Group Notice," Jiangxi Provincial People's Government Gazette, no. 18, pp. 34-35, 2019. [2] H. Wang, "Exploring brand attachment dynamics in live streaming platforms: A tiktok perspective in the digital knowledge economy," Journal of the Knowledge Economy, vol. 16, no. 2, pp. 5967–5998, 2024. https://doi.org/10.1007/s13132-024-01786-3 [3] y. Zhang and x. Yang, "On the "e-commerce + live" new marketing model," Academic Communication, no. 04, pp. 100- 110, 2021. [4] D. Yuzhe, "Research on the current problems and countermeasures of fan economy marketing in the live broadcast model--Take Jitterbug as an example," National Distribution Economy, vol. 9, pp. 4-7, 2023. https://doi.org/10.16834/j.cnki.issn1009-5292.2023.09.017 [5] Z. Embong et al., "Specific detection of fungal pathogens by 18S rRNA gene PCR in microbial keratitis," BMC Ophthalmology, vol. 8, pp. 1-8, 2008. [6] M. J. M. Kamil, S. Z. Abidin, and O. H. Hassan, "Assessing the attributes of unconscious interaction between human cognition and behavior in everyday product using image-based research analysis," in Research into Design for a Connected World: Proceedings of ICoRD 2019 Volume 1, 2019. [7] J. N. Sheth, B. I. Newman, and B. L. Gross, "Why we buy what we buy: A theory of consumption values," Journal of Business Research, vol. 22, no. 2, pp. 159-170, 1991. https://doi.org/10.1016/0148-2963(91)90050-8 [8] Y. Wu and H. Huang, "Influence of perceived value on consumers’ continuous purchase intention in live-streaming e- commerce—Mediated by consumer trust," Sustainability, vol. 15, no. 5, p. 4432, 2023. https://doi.org/10.3390/su15054432 [9] N. Toyong, S. Z. Abidin, and S. h. Mokhtar, "A case for intuition-driven design expertise," in Design for Tomorrow— Volume 3: Proceedings of ICoRD 2021, 2021: Springer, pp. 117-131. [10] D. Xi, W. Xu, L. Tang, and B. Han, "The impact of streamer emotions on viewer gifting behavior: evidence from entertainment live streaming," Internet Research, vol. 34, no. 3, pp. 748-783, 2024. https://doi.org/10.1108/INTR-05- 2022-0350 [11] Y. Tian and B. Frank, "Optimizing live streaming features to enhance customer immersion and engagement: A comparative study of live streaming genres in China," Journal of Retailing and Consumer Services, vol. 81, p. 103974, 2024. https://doi.org/10.1016/j.jretconser.2024.103974 [12] X. Fan, L. Zhang, X. Guo, and W. Zhao, "The impact of live-streaming interactivity on live-streaming sales mode based on game-theoretic analysis," Journal of Retailing and Consumer Services, vol. 81, p. 103981, 2024. [13] D. Wu, X. Wang, and H. J. Ye, "Transparentizing the “black box” of live streaming: Impacts of live interactivity on viewers’ experience and purchase," IEEE Transactions on Engineering Management, vol. 71, pp. 3820-3831, 2023. https://doi.org/10.1109/TEM.2023.3237852 https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.1007/s13132-024-01786-3 https://doi.org/10.16834/j.cnki.issn1009-5292.2023.09.017 https://doi.org/10.1016/0148-2963(91)90050-8 https://doi.org/10.3390/su15054432 https://doi.org/10.1108/INTR-05-2022-0350 https://doi.org/10.1108/INTR-05-2022-0350 https://doi.org/10.1016/j.jretconser.2024.103974 https://doi.org/10.1109/TEM.2023.3237852 35 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 8: 24-35, 2025 DOI: 10.55214/2576-8484.v9i8.9197 © 2025 by the authors; licensee Learning Gate [14] Y. Zhang, K. Li, C. Qian, X. Li, and Q. Yuan, "How real-time interaction and sentiment influence online sales? Understanding the role of live streaming danmaku," Journal of Retailing and Consumer Services, vol. 78, p. 103793, 2024. https://doi.org/10.1016/j.jretconser.2024.103793 [15] Y. Liu and X. Sun, "Tourism e-commerce live streaming: the effects of live streamer authenticity on purchase intention," Tourism Review, vol. 79, no. 5, pp. 1147-1165, 2024. https://doi.org/10.1108/TR-04-2023-0245 [16] R. Hamidah, C. H. Pangaribuan, and C. Luhur, "Enhancing purchase intention in tiktok live-stream: the roles of streamers’ credibility, interactivity, and perceived risk among generation z buyers," Jurnal Sosial Humaniora, vol. 15, no. 2, pp. 128-141, 2024. https://doi.org/10.30997/jsh.v15i2.10539 [17] X. Yingqing, N. A. M. Hasan, and F. M. M. Jalis, "Purchase intentions for cultural heritage products in E-commerce live streaming: An ABC attitude theory analysis," Heliyon, vol. 10, no. 5, p. 202, 2024. https://doi.org/10.1057/s41599-024-02690-6 [18] L. Li, K. Kang, Y. Feng, and A. Zhao, "Factors affecting online consumers’ cultural presence and cultural immersion experiences in live streaming shopping," Journal of Marketing Analytics, vol. 12, no. 2, pp. 250-263, 2024. https://doi.org/10.1057/s41270-022-00192-5 [19] G. Li, Y. Jiang, and L. Chang, "The influence mechanism of interaction quality in live streaming shopping on consumers’ impulsive purchase intention," Frontiers in Psychology, vol. 13, p. 918196, 2022. https://doi.org/10.3389/fpsyg.2022.918196 [20] M. Yi, M. Chen, and J. Yang, "Understanding the self-perceived customer experience and repurchase intention in live streaming shopping: Evidence from China," Humanities and Social Sciences Communications, vol. 11, no. 1, pp. 1-13, 2024. https://doi.org/10.1057/s41599-024-02690-6 [21] M. G. Gallarza and I. G. Saura, "Value dimensions, perceived value, satisfaction and loyalty: An investigation of university students’ travel behaviour," Tourism Management, vol. 27, no. 3, pp. 437-452, 2006. https://doi.org/10.1016/j.tourman.2004.12.002 [22] D. L. Jackson, "Revisiting sample size and number of parameter estimates: Some support for the N: Q hypothesis," Structural Equation Modeling, vol. 10, no. 1, pp. 128-141, 2003. https://doi.org/10.1207/S15328007SEM1001_6 [23] J. F. Hair, Multivariate data analysis, 7th ed. Upper Saddle River, NJ: Prentice Hall, 2009. [24] H. F. Kaiser, "An index of factorial simplicity," Psychometrika, vol. 39, no. 1, pp. 31-36, 1974. https://doi.org/10.1007/BF02291575 [25] M. J. Norusis, SPSS for windows: Professional statistics user's guide, release 5.0. Chicago, IL: SPSS Incorporated, 1992. [26] A. K. Kohli, T. A. Shervani, and G. N. Challagalla, "Learning and performance orientation of salespeople: The role of supervisors," Journal of Marketing Research, vol. 35, no. 2, pp. 263-274, 1998. https://doi.org/10.2307/3151853 [27] W. W. Chin, "The partial least squares approach to structural equation modeling," Modern methods for business research, vol. 295, no. 2, pp. 295-336, 1998. [28] G. A. Churchill Jr, "A paradigm for developing better measures of marketing constructs," Journal of Marketing Research, vol. 16, no. 1, pp. 64-73, 1979. [29] C. Fornell and D. F. Larcker, "Evaluating structural equation models with unobservable variables and measurement error," Journal of Marketing Research, vol. 18, no. 1, pp. 39-50, 1981. https://doi.org/10.1177/002224378101800104 [30] H. Xiong, J. Zhang, B. Ye, X. Zheng, and P. Sun, "A model analysis of the impact of common method variation and its statistical control pathways," Advances in Psychological Science, vol. 20, no. 5, pp. 757-769, 2012. https://doi.org/10.3724/sp.j.1042.2012.00757 https://doi.org/10.1016/j.jretconser.2024.103793 https://doi.org/10.1108/TR-04-2023-0245 https://doi.org/10.30997/jsh.v15i2.10539 https://doi.org/10.1057/s41599-024-02690-6 https://doi.org/10.1057/s41270-022-00192-5 https://doi.org/10.3389/fpsyg.2022.918196 https://doi.org/10.1057/s41599-024-02690-6 https://doi.org/10.1016/j.tourman.2004.12.002 https://doi.org/10.1207/S15328007SEM1001_6 https://doi.org/10.1007/BF02291575 https://doi.org/10.2307/3151853 https://doi.org/10.1177/002224378101800104 https://doi.org/10.3724/sp.j.1042.2012.00757